Dry spell characteristics over India based on IMD and APHRODITE datasets

Sushama, Laxmi; Said, S. Ben; Khaliq, M. N.; Kumar, D. Nagesh; Laprise, René

doi:10.1007/s00382-014-2113-9

Cited by 36 publications

(30 citation statements)

References 32 publications

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“…It is worthwhile to note the reversal of trends in F DS from initial to last window, especially in PI, WC, and HR1. Most of the past studies also demonstrated that the positive trend of prolonged dry days happened in the past half century [ Dash et al , ; Singh et al , ; Sushama et al , ]. The main factor attributing to these changes is the impact of global warming on the hydrological cycle and subsequent shift in the monsoon patterns [ Giorgi et al , ].…”

Section: Resultsmentioning

confidence: 99%

“…However, major challenges in POT approach are (i) ensuring independence between the peaks and (ii) fixing an adequate threshold [Tanaka and Kaoru, 2002]. Past studies have adopted two types of thresholds viz., absolute threshold [Goswami et al, 2006;Sushama et al, 2014] and varying (percentile) threshold [Krishnamurthy et al, 2009;Vittal et al, 2013;Mondal and Mujumdar, 2015]. Among these, varying threshold is most suitable to demarcate extreme events in a gridwise analysis, since the characteristics of extreme events are highly influenced by the geographical locations in a heterogeneous rainfall region.…”

Section: Extraction Of Extreme Wet and Dry Spellsmentioning

confidence: 99%

“…Most studies considered a threshold ≥ 2.5 mm/d to demarcate a rainy day, assuming that a rainfall lesser than 1 mm/d contributes only to evaporation [Epifani et al, 2004;Tilya and Mhita, 2007]. A few studies analyzing wet and dry spells considered even 1.5 mm/d [Harrington and Flannigan, 1993], 2 mm/d [Perzyna, 1994], 2.5 mm/d [Ratan and Venugopal, 2013], and 3 mm/d [Sushama et al, 2014] to avoid the possible underestimation of dry spell because of meagre rainfall events. In addition, a threshold of 2.5 mm/d has been frequently be used for defining a dry day in the context of agriculture and the onset of monsoon [Pai and Rajeevan, 2007;Dash et al, 2009].…”

Section: Extraction Of Extreme Wet and Dry Spellsmentioning

confidence: 99%

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Changing characteristics of extreme wet and dry spells of Indian monsoon rainfall

2016

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Modeling of extreme events and its dynamic behavior have always been an intriguing topic. Increase in the magnitude and frequency of extreme events has widely been reported in recent decades, which is attributed to abrupt changes in climate. Numerous studies on extreme Indian monsoon characteristics, using a coarse‐resolution data set, have pointed out significant changes in heavy precipitation pattern over India. However, these studies differ in their conclusions, emphasizing the need for a fine‐resolution analysis. The present study aims to analyze the spatiotemporal variations and trends in the extreme (wet and dry) Indian monsoon precipitation, using 0.25° × 0.25° high‐resolution gridded data for a period of 113 years (1901–2013). Significant increase in the maximum intensity of rainfall and spatial heterogeneity is observed over the past half century. In addition, significant negative trends in wet spell durations and positive trends in dry spell durations are observed over wet regions; whereas contrasting trends are observed over dry regions. A shift in the frequency distribution of extreme events during the monsoon period is also noticed. The 50 year return level of maximum intensity clearly shows positive trends over the past century. Though characteristics of extremes are observed to be highly localized, apparent signs of wet regions turning drier and dry regions turning wetter are obtained. A comprehensive insight into different characteristics (intensity, spell, onset, and frequency) of Indian monsoon extremes is provided, which will help in effective water resources management and flood/drought hazard preparedness.

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Section: Resultsmentioning

confidence: 99%

Section: Extraction Of Extreme Wet and Dry Spellsmentioning

confidence: 99%

Section: Extraction Of Extreme Wet and Dry Spellsmentioning

confidence: 99%

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Changing characteristics of extreme wet and dry spells of Indian monsoon rainfall

2016

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“…The gridded ratio is then multiplied by the gridded climatology for each day to obtain the gridded daily precipitation. The comparison of AHPRODITE data with other observational data sets shows that the APHRODITE data can effectively depict the precipitation pattern and variability over the Indian monsoon region (Salvi et al ., ; Pai et al ., ; Sushama et al ., ; Prakash et al ., ).…”

Section: Methodsmentioning

confidence: 99%

Can statistical downscaling improve consensus among CMIP5 models for Indian summer monsoon rainfall projections?

Madhusoodhanan

Shashikanth

Eldho

et al. 2017

Intl Journal of Climatology

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The projections of plausible changes in future climate using coarse scale General Circulation Models (GCMs) for non‐smooth climate variables, such as precipitation, possess limited skill and are often conflicting. The confidence in precipitation projections is further obscured due to the exclusion of inter‐model and natural internal variability. The present study investigates how far statistical downscaling can provide confidence in future projections while incorporating these major uncertainties under a moderate radiative forcing scenario. Here, we assess the consensus for future changes in Indian Summer Monsoon Rainfall (ISMR) from 20 CMIP5 GCMs and their statistically downscaled counterparts at 0.05° resolution for the 21st century. The Statistical Downscaling (SD) model is skillful in capturing the spatial variability of observed ISMR and shows significant improvement over host GCMs. GCMs show consistent but spatially inhomogeneous increase in future ISMR which intensify towards the end of the century. On the other hand, the downscaled outputs show spatially non‐uniform trends which intensify in their respective directions from near to long term. Both projections show high inter‐model inconsistencies. The multi‐model consensus among GCMs and SD across these in‐congruent models depicts inconclusive and highly uncertain changes in future. The GCMs show significant change with high model inconsistency uniformly across India across time scales. Even though the downscaled outputs show similar results for majority of the Indian landmass, it is highly heterogeneous across time horizons. There is also an emergence of medium evidence for future changes in ISMR for few regions of the country such as the southern Western Ghats, foothills of the Himalaya and central India. The study brings out that even‐improved simulations from downscaling fail to reliably project ISMR due to high inter‐model uncertainty and internal variability. The significant but inconsistent future changes in ISMR projected for a major portion of the Indian subcontinent, in contrast to earlier studies, pose extreme challenges to climate change impact assessments and adaptation/mitigation planning.

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“…CGCM3.1 model is selected for the present study due to the (i) availability of CMIP3 (Coupled Model Intercomparison Project 3) projections (IPCC, 2007a) of predictor variables and (ii) the ability of CCCma models to simulate the inter-annual variability of Indian monsoon rainfall (Kripalani et al, 2007) and simulate the spatial patterns of the dry spell characteristics of annual and summer monsoon rainfall over India (Sushama et al, 2014). Among the two resolution outputs provided by CCCma, T47 (3.75°) and T63 (2.8°), T47 is selected for this study.…”

Section: Study Region and Data Descriptionmentioning

confidence: 99%

Dynamic coupling of support vector machine and K-nearest neighbour for downscaling daily rainfall

Devak

Dhanya

Gosain

2015

Journal of Hydrology

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Dry spell characteristics over India based on IMD and APHRODITE datasets

Cited by 36 publications

References 32 publications

Changing characteristics of extreme wet and dry spells of Indian monsoon rainfall

Changing characteristics of extreme wet and dry spells of Indian monsoon rainfall

Can statistical downscaling improve consensus among CMIP5 models for Indian summer monsoon rainfall projections?

Dynamic coupling of support vector machine and K-nearest neighbour for downscaling daily rainfall

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